6.5 RIS3 social media analysis

6.5 RIS3 social media analysis

This online tool offers simple indicators on the RIS3 process by using data coming from social media sites, such as Twitter or Google trends with regards the overall process, or also allow for specific searches with keywords of key policies or programmes implemented in the region. Using natural language ontologies, it could allow for cross-regional comparisons or S3 social ‘interest’. However, the mapping exercise did not identify any pre-existing method that is relevant to describe social media analysis in RIS3.

As reported by Charalabidis et al. (2012b), “a considerable body of knowledge on how social media can be used effectively by enterprises for supporting and strengthening various functions, such as marketing, customer relationships, new product development, etc.”. Unfortunately, “in contrast, the benefits of social media and the application of social media analytics for governmental organizations has not been observed comparably” (Gotsch and Grubmuller, 2013)

Current research on the use of social media analytics in public sector reveals indeed that “governments are just about to explore the opportunities that [are offered by] social media and the analysis of user-generated contents” (Grubmüller et al., 2013). This is particularly evident in the field of RIS3, in which the application of social media analytics has the potential to track and capture satisfaction of beneficiaries with the RIS3 process overall and increase collaboration. Indeed, according to the Joint Research Centre’s Institute for Prospective Technological Studies (Osimo, 2008; Pascu, 2008; Pascu et al. 2007; 2008), ICT-enabled solutions for the application of social media analytics can effectively support and facilitate the development and implementation process of Smart Specialisation Strategies.

However, the lack of case studies and literature dealing with the use of this method in the context of Smart Specialisation Strategies makes it difficult to explore its practical implications.

Description of the method

Despite this situation, however, useful insights into how to use social media analytics in the development of Smart Specialisation Strategies can be acquired by looking at the several research projects implemented over the years in the public sector thanks to the European Commission’s financial support. In the last decade, the European Commission has indeed expressed a strong interest in this subject and funded several projects aimed at building ICT-based SMA (social media analytic) tools for gathering feedback, detecting future trends, stimulating collaboration and supporting evidence-based decision and policy making. Some interesting examples are:

The tools developed by these projects are considered of great value to the ONLINE-S3 project because they highlight the potentials offered by the analysis of contents extracted by social media. Moreover, they provide useful knowledge and lessons on how to develop the ICT-driven services, tools and applications for RIS3 social media analytics which are going to populate the ONLINE Platform for Smart Specialisation Policy Advice.

The project has produced a fully automated ICT-based SMA platform which allow decision-makers to analyse the vast amount of user-generated contents and opinions sourced from social media and blogs and use this data for supporting the development of public policies and strategies (http://cordis.europa.eu/project/rcn/102148_en.html). The architecture of the platform is shown in Figure 25 and its main components are the following: 1) Policy Modeling Tool; 2) Data Acquisition Module; 3) Linguistic Processing Pipeline; 4) Opinion Mining & Argument Extraction; 5) Argument Summarization; 6) Social Reaction Visualization (Charalabidis et al., 2012b).

PADGETS has developed a prototype service for policy makers that uses social media technologies and techniques to: boost public engagement; enable cross-platform publishing and content tracking; and provide support to decision making. With the PADGETS platform, messages related to public policies can be disseminated simultaneously within multiple social media sites, but using a single integrated interface. This makes it possible to reach large user groups. Their feedback is then collected by the platform, that keeps track of and analyses the users’ reaction by each message (Ferro et al., 2010). This system can therefore empower “citizens to better participate in decision making processes and public deliberation of policies by increasing the impact of their opinions [and feedbacks] while dramatically reducing the effort and cost of projecting them in the public sphere” (http://www.padgets.eu).

ImmigrationPolicy2.0 (Participatory Immigration Policy Making and Harmonization based on Collaborative Web2.0 Technologies)

ImmigrationPolicy2.0 has developed and piloted “a range of citizen-centric services which facilitate the participation of citizens in the migration policy development process. […] The services support [the] drafting [of] immigration policy text, develop and test policy models, evaluate scenarios (“what-if” process), as well as harmonizing policy text”. Moreover, they facilitate “societal group of immigrants with migration background […] to get informed” and can be used to “evaluate various migration-related proposals and policies […]. The process [involved] the development of new models for citizens’ legal residents’ participation, as well as the collection and analysis of collaborative input from multiple citizens. The pilot services of the project [was] offered over a centralized, collaborative, trustful migration platform (conveniently called ImmigrationPolicy2.0 platform) enabling users (i.e. policy makers, politicians, decision makers, citizens) to identify, model, visualise, analyse and evaluate national migration policies, practices, monitor and accordingly harmonise easily their procedures and data formats relateding to their economic activities and documents involved (e.g. civil status documents i.e. (e.g. residence permits/certificates, work permits, civil status certificates, family unification certificates)” (http://cordis.europa.eu/project/rcn/191809_en.html).

Puzzled by Policy

Puzzled by Policy aimed “to address the specific issue of immigration in a manner that likewise addresses the broader issue of widespread confusion about and disengagement from the policymaking process by providing all citizens [with] an informative and easy-to-use platform to engage topical policy issues”. To achieve this aim, the project’s team has developed, “tried and tested eParticipation […] tools such as EU Profiler and Debategraph with new widget applications to reduce the complexity of decision making at the EU level and ‘push’ the platform to popular social media sites such as Facebook and Twitter as well as to their own desk top and mobile devices” (http://www.puzzledbypolicy.eu).

UniteEurope

UniteEurope has developed a software that analyses social media contents related to migrant integration at the city level. “Through a sophisticated and automatized filtering of public social media contents, [this] tool [can] analyse migrant integration specific posts produced by citizens, communities, and organizations”. The software helps policy makers to “judge on current discussions on migrant integration issues; to proactively or reactively take actions; and to monitor effects on actions taken” (UniteEurope Consortium, 2014a). The UniteEurope tool supports sources of three different stream types: Twitter, Facebook and Google+ (UniteEurope Consortium, 2014b).

Usability and impact

Considering the usability and impact of social media analytics in the public sector, which are shown by the above-mentioned projects, the use of this method in relation to the design and implementation of Smart Specialisation Strategies can provides the governance system with the possibility to integrate valuable stakeholders’ insights, opinions and feedback, detect future trends, stimulate collaboration and support evidence-based decision making processes by taking public opinion into account. This method can also help increase transparency (United Nations – Department of Economic and Social Affairs, 2012; European Commission 2016). What is more, the World Bank (2012) reports that social media platforms generate higher public participation rates than other conventional e-government applications. These tools can play an even greater role in strengthening and widening the participation of and interaction with citizens (Charalabidis et al. 2012a; 2012b). Moreover, in the RIS3 context, social media analytics can help articulate the collaborative visioning process and better legitimate the selection of specific scenarios emerging during the strategy formulation.

Moreover, even if this aspect does not significantly emerge from the projects previously discussed, when talking about using user-generated contents extracted from social media in the public sector and in the RIS3 development process, one major implication is privacy. This generates some fundamental concerns for public authorities “seeking to develop their own social media [groups] or strategies for engaging with other sites […]. Users willingly share their identities on social media sites such as Facebook and Twitter, yet this does not mean they do not care what happens to this information. Indeed, users have serious concerns about how secondary firms use their information as a source for data mining and surveillance […], and the extent to which social media sites passively facilitate or actively encourage these activities” (Kietzmann et al., 2011). This may result in legal actions for invasion of privacy (Kravets, 2010).

Relevant data sources

The European project NOMAD (Policy Formulation and Validation through non-moderated crowdsourcing) offers a list of the 50 most used social media from which the user-generated contents can be collected (Xenakis et al., 2012). The list is reported in Table 7. Considering that no case studies can be identified in which social media analytics has been used in the field of RIS3, this table represents a list of potential data sources to be considered and tested.

Table 7 List of the 50 most used social media

N°

NAME

FOCUS DESCRIPTION

TOP POPULARITY

REGISTRATION

UNIQUE USERS

1

Facebook

Social Networking Service

Worldwide

Open > 13

845.000.000

2

Qzone

Social Networking Service

China

Open

536.000.000

3

Youtube

Video sharing

Worldwide

Open

490.000.000

4

Twitter

Social Networking Service/Microblogging

US

Open

380.000.000

5

Windows Live

Social Networking Service

Worldwide

Open

330.000.000

6

Wikipedia

Encyclopedia

Worldwide

Open

310.000.000

7

Blogger

News ‐ Bookmarking

Worldwide

Open

300.000.000

8

Habbo

Social Networking Service

Worldwide

Open > 13

230.000.000

9

Skype

Voice Calls/Instant Messages

Worldwide

Open

200.000.000

10

Yahoo! Answers

Question-and-Answer

Worldwide

Open > 13

200.000.000

11

Renren

Social Networking Service

China

Open > 18

160.000.000

12

Badoo

Social Networking Service

EU (Italy)

Open > 18

121.000.000

13

Vkontakte

Social Networking Service

Russia

Open

121.000.000

14

Bedo

Social Networking Service

Worldwide

Open > 13

117.000.000

15

Yahoo! News

News

US

Open > 13

110.000.000

16

LinkedIn

Professional Social Networking Service

US

Open > 18

100.000.000

17

Google+

Business Social Networking Service

US

Open > 13

100.000.000

18

Myspace

Social Networking Service

Worldwide

Open > 13

100.000.000

19

Orkut

Social Networking Service

Brazil

Open > 18

100.000.000

20

Tagged

Social Networking Service

US

Open

100.000.000

21

Scribd

Document Sharing

US

Open

100.000.000

22

Friendster

Social Gaming

Asia

Open > 18

90.000.000

23

hi5

Social Networking Service

India

Open > 13

80.000.000

24

CNN

News

US

Open

74.000.000

25

MSNBC

News

US

Open

73.000.000

26

Netlog

Social Networking Service

India

Open > 13

70.000.000

27

Google News

News

US

Open

65.000.000

28

Flixster

Social Networking Service

US

Open > 13

63.000.000

29

New York Times

News

US

Open

59.500.000

30

HuffingtonPost

News/Blogging

US

Open

54.000.000

31

MyLife

Social Networking Service

US

Open

51.000.000

32

Classmates.com

Social Networking Service

US

Open > 18

50.000.000

33

Douban

Online music, movie and book database

China

Open

46.850.000

34

Odnoklassniki

Social Networking Service

Russia/Ukraine

Open

45.000.000

35

Viadeo

Profeessional Social Networking Service

Worldwide

Open > 18

35.000.000

36

Reddit

Social News

Worldwide

Open

34.879.881

37

Flickr

Video/Image sharing

Worldwide

Open > 13

32.000.000

38

Fox News

News

US

Open

32.000.000

39

Last.fm

Music

US

Open

30.000.000

40

MyHeritage

Social Networking Service

US

Open

30.000.000

41

WeeWorld

Virtual World

US

Open > 13

30.000.000

42

Xanga

Blog

US/Hong Kong

Open

27.000.000

43

Digg

News-Bookmarking

Worldwide

Open

25.100.000

44

Washington Post

News

US

Open

25.000.000

45

LATimes

News

US

Open

24.900.000

46

Mail Online/Daily Mail

News

US/UK

Open

24.800.000

47

Mixi

Social Networking Service

Japan

Invite Only

24.323.160

48

Reuters

Business and Finances News

US

Open

24.000.000

49

Cyworld

Social Networking Service

South Korea/China

Open > 25

24.000.000

50

Gaia Online

Social Gaming

US

Open > 13

23.523.663

Source: Xenakis et al. (2012)

Implementation roadmap

A possible implementation roadmap for social media analytics in the development of RIS3 is presented in Figure 26 Social media analytics process. This roadmap is based on the analysis of the previous projects and research by Fan and Gordon (2014). ICT-enabled solutions for social media analytics can facilitate the development and implementation of this roadmap.

Figure 26 Social media analytics process

Source: Fan and Gordon (2014)

Phase 1. Capture

Relevant social media data are obtained by monitoring or listening to various social-media sources. This stage “helps identify conversations on social media platforms related to” the RIS3 process and can cover “popular platforms (such as Facebook, Foursquare, Google+, LinkedIn, Pinterest, Twitter, Tumblr, and YouTube), as well as smaller, more specialized sources (such as Internet forums, blogs, microblogs, wikis, news sites, picture-sharing sites, podcasts, and social-bookmarking sites)”. Data collected from the selected sources is used to prepare a dataset which is going to be used in Phase 2. “Various preprocessing steps may be performed, including data modeling, data and record linking from different sources, stemming, part-of-speech tagging, feature extraction, and other syntactic and semantic operations that support analysis” (Fan and Gordon, 2014).

Phase 2. Understand

After collecting the conversations related to the RIS3 process, their meaning needs to be assessed and the metrics for decision making needs to be developed. “Since the capture stage gathers data from many users and sources, a sizeable portion may be noisy and thus has to be removed prior to meaningful analysis. Simple, rule-based text classifiers or more sophisticated classifiers trained on labeled data may be used for this cleaning function. Assessing meaning from the cleaned data can involve statistical methods and other techniques derived from text and data mining, natural language processing, machine translation, and network analysis. The understand stage provides information about user sentiment and their behavior […]. Many useful metrics and trends about users can be produced in this stage, covering their backgrounds, interests, concerns, and networks of relationships” (Fan and Gordon, 2014).

Phase 3. Present

“The results from different analytics are summarized, evaluated, and shown to users in an easy-to-understand format. Visualization techniques may be used to present useful information; one commonly used interface design is the visual dashboard, which aggregates and displays information from multiple sources. Sophisticated visual analytics go beyond the simple display of information. By supporting customized views for different users, they help make sense of large amounts of information, including patterns that are more apparent to people than to machines. Data analysts and statisticians may add extra support” (Fan and Gordon, 2014).